WiFi Fingerprint Indoor Positioning System Based on IWSO-CNN-Transformer

With the rapid development of location-based services, indoor positioning has become a research hotspot. Channel State Information (CSI) has emerged as a promising signal source for high-precision indoor localization. However, existing methods still face challenges in robustness and accuracy. This paper proposes a WiFi CSI fingerprinting model that performs outlier removal and denoising in the offline stage. After processing, the amplitude and phase of CSI are fused and input into a CNN-Transformer network to capture spatial features and global dependencies. To address the difficulty of hyperparameter tuning in Transformers, an Improved White Shark Optimization (IWSO) algorithm is introduced to optimize key parameters and enhance performance. In the online stage, positioning is achieved through real-time CSI measurement. Experimental results show that the proposed method improves positioning accuracy by 18.33% compared to models without hyperparameter tuning, achieving an average positioning error of 1.35m.